In acute ischemic stroke, large vessel occlusions of the anterior circulation are increasingly treated with endovascular therapy (EVT). The efficacy of this therapy depends on adequate treatment selection. Treatment decisions can be based on predictions of functional outcome. Most existing studies predict functional outcomes using clinical parameters. We set out to study functional outcome prediction performance by integrating imaging in a multimodal setting. Using a multi-center dataset containing 2927 patients, we compare the functional outcome prediction performances of clinical baseline models, including the clinically validated MR PREDICTS decision tool, image-based models with deep learning networks, and a multimodal approach combining clinical and imaging information. The predicted outcome measure is dichotomized modified Rankin Scale score 90 days after EVT. We perform sanity checks, hyperparameter optimization, and comparisons of effectiveness of using CTA, NCCT, or both images as input. Our experiments show that information extracted from CTA or NCCT images does not significantly improve the performance, as quantified using AUC, of functional outcome prediction methods compared to a baseline model. The multimodal approach may replace radiologically derived biomarkers, as its performance is non-inferior.

Introduction: Thromboembolic conditions are a leading global cause of mortality and a major cause of disability. Throughout the years mechanical thrombectomy has become a preferred method of treatment. Removing thrombus in its entirety on first pass decreases procedure time as well as lowers the risk of distal embolization. Areas covered: This review provides a comprehensive overview and classification of the patent literature on devices for non-fragmented thrombus removal via grip. Patentscope database was used to search for internationally granted patents published any time before the access date (October 2024). The search using keywords and patent classification code led to identifying 141 relevant patents that were then categorized based on location and type of grip they describe. Expert opinion: The designs found are analyzed in the discussion and a broader context for their relevance is given in the expert opinion section. The following review can provide insight into possible mechanical thrombectomy methods, general trends in the field as well as serve as an inspiration in development of novel devices.

Background: The accurate automated extraction of brain vessel centerlines from Computed tomographic angiography (CTA) images plays an important role in diagnosing and treating cerebrovascular diseases such as stroke. Despite its significance, this task is complicated by the complex cerebrovascular structure and heterogeneous imaging quality. Purpose: This study aims to develop and validate a segmentation-assisted framework designed to improve the accuracy and efficiency of brain vessel centerline extraction from CTA images. We streamline the process of lumen segmentation generation without additional annotation effort from physicians, enhancing the effectiveness of centerline extraction. Methods: The framework integrates four modules: (1) pre-processing techniques that register CTA images with a CT atlas and divide these images into input patches, (2) lumen segmentation generation from annotated vessel centerlines using graph cuts and robust kernel regression, (3) a dual-branch topology-aware UNet (DTUNet) that optimizes the use of the annotated vessel centerlines and the generated lumen segmentation via a topology-aware loss (TAL) and its dual-branch structure, and (4) post-processing methods that skeletonize and refine the lumen segmentation predicted by the DTUNet. Results: An in-house dataset derived from a subset of the MR CLEAN Registry is used to evaluate the proposed framework. The dataset comprises 10 intracranial CTA images, and 40 cube CTA sub-images with a resolution of (Formula presented.) voxels. Via five-fold cross-validation on this dataset, we demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV). Specifically, it achieves an ASCD of 0.84, an (Formula presented.) of 0.839, and an (Formula presented.) of 0.885 for intracranial CTA images, and obtains an ASCD of 1.26, an (Formula presented.) of 0.779, and an (Formula presented.) of 0.824 for cube CTA sub-images. Subgroup analyses further suggest that the proposed framework holds promise in clinical applications for stroke diagnosis and treatment. Conclusions: By automating the process of lumen segmentation generation and optimizing the network design of vessel centerline extraction, DTUnet achieves high performance without introducing additional annotation demands. This solution promises to be beneficial in various clinical applications in cerebrovascular disease management.

Purpose: The assessment of collateral status may depend on the timing of image acquisition. The purpose of this study is to investigate whether there are optimal time points in CT Perfusion (CTP) for collateral status assessment, and compare collaterals scores at these time points with collateral scores from multiphase CT angiography (mCTA). Methods: Patients with an acute intracranial occlusion who underwent baseline non-contrast CT, mCTA and CT perfusion were selected. Collateral status was assessed using an automatically computed Collateral Ratio (CR) score in mCTA, and predefined time points in CTP acquisition. CRs extracted from CTP were correlated with CRs from mCTA. In addition, all CRs were related to baseline National Institutes of Health Stroke Scale (NIHSS) and Alberta Stoke Program Early CT Score (ASPECTS) with linear regression analysis to find the optimal CR. Results: In total 58 subjects (median age 74 years; interquartile range 61–83 years; 33 male) were included. When comparing the CRs from the CTP vs. mCTA acquisition, the strongest correlations were found between CR from baseline mCTA and the CR at the maximal intensity projection of time-resolved CTP (r = 0.81) and the CR at the peak of arterial enhancement point (r = 0.78). Baseline mCTA-derived CR had the highest correlation with ASPECTS (β = 0.36 (95%CI 0.11, 0.61)) and NIHSS (β = − 0.48 (95%CI − 0.72, − 0.16)). Conclusion: Collateral status assessment strongly depends on the timing of acquisition. Collateral scores obtained from mCTA imaging is close to the optimal collateral score obtained from CTP imaging.